880022 :Social Data Mining (HAIT/DJ/BDM)

General info

Instruction language English
Type of Instruction Lectures and hands-on sessions (Lecture schedule)
Type of exams Two-weekly tests and final exam (Examination schedule)
Course load:6 ECTS credits
Registration:Enrollment via Blackboard
Blackboard InfoLink to Blackboard (When you see 'Guest are not allowed in this course', please login at Blackboard itself)


prof. dr. E.O. Postma (coordinator (unit 1))
No photo available
C.D. Emmery MSc (unit 1 and 3)

dr. G.A. Chrupala (coordinator (unit 3))


The three learning goals for the course are:

1. The student is able to understand the main principles of data collecting and data mining methods. 

2. The student knows how to pre-process and analyse social data on a large scale.

3. The student is able to perform and evaluate elementary data mining experiments.


Data Science methods are becoming the main tools for acquiring information both in the business context and in scientific research. The course offers a thorough introduction to the use of data mining for the analysis of data in a wide variety of formats. Upon completion of the course, students will have acquired the skills necessary to apply data mining in support of decision making, visualisation, and discovery. In addition, students will be familiar with advanced topics such as deep learning. The perspective of the course is application-oriented and serves to provide students with the knowledge and experience that is in line with the current demand for skilled data scientists. 


This course has a maximum capacity of 40 participants. Questions: e-mail to: mastercoursesCIW@uvt.nl

Social Data Mining is accessible for all students (no technical background required). During the course, students will complete assignments in which they will train their basic data mining skills in the domain of social media and behaviour. The experiments and assignments will be performed with open-source data mining software weka. During the course there will be intermediate exam every two weeks to ensure that students keep on track with the course contents. The course is completed with a written exam.

This course is compulsory for students of the track Data Science: Business and Governance (2016-2017). Passing the course is a prerequisite for Master thesis/Data Science in Action in the DSBG track.
Compulsory Reading

  1. Research papers, see Blackboard.

Recommended Prerequisites


Required Prerequisites


Compulsory for

  • Data Science: Business and Governance ( 2015, 2016 )

Recommended option for